Regional Variations in Clinical Trial Outcomes in Oncology
Bibliographic record
Abstract
BACKGROUND: It is unknown how often regional differences in oncology trials are observed. Based on our study findings, we quantified regional variation in registration studies in oncology and developed a question guide to help clinicians evaluate regional differences. METHODS: Using FDA archives, we identified registration studies in solid tumor malignancies from 2010 to 2020. We extracted the baseline study characteristics and participating countries and determined whether the primary publication reported a regional subgroup analysis. For studies presenting outcomes stratified by region, we extracted the stratified hazard ratios (HRs) and extracted or calculated the test for heterogeneity. We performed a random effects meta-analysis and a pairwise comparison to determine whether outcomes differed between high-income versus mixed-income regions. RESULTS: We included 147 studies in our final analysis. Studies supporting FDA drug approval have become increasingly multinational over time (β = 0.5; P=.04). The median proportion of countries from high-income groups was 81.2% (range, 44%-100%), with no participation from low-income countries in our cohort. Regional subgroup analysis was presented for 78 studies (53%). Regional heterogeneity was found in 17.8% (8/45) and 18% (8/44) of studies presenting an overall survival (OS) and progression-free survival endpoint, respectively. After grouping regions by income level, we found no difference in OS outcomes in high-income regions compared with mixed-income regions (n=20; HR, 0.95; 95% CI, 0.84-1.07). To determine whether regional variation is genuine, clinicians should evaluate the data according to the following 5 questions: (1) Are the regional groupings logical? (2) Is the regional difference on an absolute or relative scale? (3) Is the regional difference consistent and plausible? (4) Is the regional difference statistically significant? (5) Is there a clinical explanation? CONCLUSIONS: As registration studies in oncology become increasingly international, regional variations in trial outcomes may be detected. The question guide herein will help clinicians determine whether regional variations are likely to be clinically meaningful or statistical anomalies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.355 | 0.608 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".